DDD (Domain Driven Design) aplicado ao e-commerce - Nelson Senna

Test on Microsrvices

DRY on Microservices

Don't violate DRY within a microservice, but be relaxed about violating DRY across all services. The evils of too much coupling between services are far worse than the problems caused by code duplication.

Shared Model

Core Responsibilities of An Evolutionary Architecth

Interplay of goals, principles, and practices in a very clear format.

Peixes não sobem em árvores!

Pattern Recognition

A flow chart of the process of classifier design

Probabilistic distance measures

Dissimilarity measures for numeric variables (between x and y)

Linear regression

Statistical Pattern Recognition

Stages in a pattern recognition problem

1. Formulation of the problem: gaining a clear understanding of the aims of the investigation and planning the remaining stages.

2. Data collection: making measurements on appropriate variables and recording details of the data collection procedure (ground truth).

3. Initial examination of the data: checking the data, calculating summary statistics and producing plots in order to get a feel for the structure.

4. Feature selection or feature extraction: selecting variables from the measured set that are appropriate for the task. These new variables may be obtained by a linear or nonlinear transformation of the original set (feature extraction). To some extent, the division of feature extraction and classification is artificial.

5. Unsupervised pattern classification or clustering. This may be viewed as exploratory data analysis and it may provide a successful conclusion to a study. On the other hand, it may be a means of preprocessing the data for a supervised classification procedure.

6. Apply discrimination or regression procedures as appropriate. The classifier is de- signed using a training set of exemplar patterns.

7. Assessment of results. This may involve applying the trained classifier to an indepen- dent test set of labelled patterns.

8. Interpretation.

Pattern classifier

The pattern recognition cycle

Feature selection and extraction

Correlation

Tree replication problem. The same subtree can appear at different branches.

This makes the decision tree more complex than necessary and perhaps more difficult to interpret. Such a situation can arise from decision tree implementations that rely on a single attribute test condition at each internal node. Since most of the decision tree algorithms use a divide-and-conquer partitioning strategy, the same test condition can be applied to different parts of the attribute space, thus leading to the subtree replication problem.

Comparison among the impurity measures for binary classification problems

Test condition for continuous attributes

Test conditions for nominal attributes

Classifying an unlabeled vertebrate. The dashed lines represent the outcomes of applying various attribute test conditions on the unlabeled vertebrate. The vertebrate is eventually assigned to the Non-mammal class

Training and test error rates

Example of a decision tree and its decision boundaries for a two-dimensional data set